Safeguarding Text-to-Image Generative Models Against Unauthorized Knowledge Distillation
Closed-weight generative services are increasingly deployed through query-based APIs, where users can obtain generated outputs while model parameters remain inaccessible. However, such deployment does not prevent model stealing: an attacker can repeatedly query the service, collect large volumes of released synthetic images, and use them as training data for a private substitute model. This query-output-driven process enables unauthorized knowledge distillation and capability replication without direct access to the original weights. To mitigate this threat, a practical defense should preserve the visual fidelity of released images, provide explicit control over perturbation magnitude, an...
| Authors | Yilan Gao, Sida Huang, Hongyuan Zhang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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